The present invention relates generally to x-ray systems and methods, and more particularly to systems and methods that utilize the classification of structures in a dataset to guide the selection of processing paths to reduce and eliminate blur from images.
Digital tomosynthesis is widely used for three-dimensional (3D) reconstruction of objects acquired from limited angle x-ray projection imaging using a movable x-ray tube and a stationary digital detector. It is a refinement of conventional linear tomography, which has been known since the 1930s. As with linear tomography, tomosynthesis suffers from the residual blur of objects outside the plane of interest. This tomographic blur, often caused by overlying anatomy, obscures detail in the plane of interest and limits the contrast enhancement of the projection image slices. Removing the overlying blurred anatomical structures improves contrast of in-plane structures by restricting the dynamic range of the image to a section of interest, as well as removing residual structures that may have frequency content similar to an object of interest in that section. At a fundamental level, the point spread function (PSF) of a tomosynthesis system characterizes the spread of blur in the imaging volume. The PSF of a tomosynthesis system is shift-variant in nature. However, completely eliminating blur is a non-trivial task. It is computationally complex and intensive because of the extent of the PSF, it is not easy to eliminate blur.
Tomosynthesis allows the retrospective creation of an arbitrary number of section images from a single pass of an x-ray tube. Projection images are acquired during an x-ray scan over a limited angle for reconstruction into full 3D volume images. To improve the presentation quality of the reconstruction slices, the tomosynthesis data is processed through a series of algorithms after reconstruction. Most prior art image processing techniques involve two-dimensional (2D) processing of the reconstructed image dataset without regard to the third dimension and prior knowledge of the imaging geometry. This is inadequate for tomosynthesis images where there can be out of focus high frequency components that can be sharpened by a presentation processing algorithm. These and many other artifacts have engineers, researchers and scientists interested in alternative ways of processing such images.
Since the true imaging geometry is represented using a 3D PSF of the dataset, in theory, it should be possible to use this information to improve the presentation processing of the reconstructed image dataset. Furthermore, if all points in the imaging volume can be classified based on whether they are in focus or out of focus, for example, in a certain plane, we can further adapt the processing algorithm based on the classification mask.
Therefore, there is a need for performing PSF based 3D presentation processing for a tomosynthesis dataset and to further adapt the processing based on a PSF based classification mask. This processing can also be used in a 3D multi-resolution processing framework.